The usage of remote control units to operate and control devices is very common. And, this is more so with home appliances. A typical home has multiple appliances such as refrigerators, air conditioners, televisions, and personal video recorders and players. Further, each appliance has a specialized remote control unit to help operate and control the related appliance. Let us consider a television remote control unit. A typical such remote provides the functional support such as for the following: browse (to browse through the channels), swap (to swap a channel with the last viewed channel), increase volume, decrease volume, jump to a particular channel, power on, power off, and set timer. The analysis of the way a remote is used to operate and control a television provides an insight into the user behavior. This insight helps greatly in general in content personalization, and in particular in content targeting, say for example, ad targeting. The requirement is to study carefully the remote usage data so as to determine the specific usage patterns hidden in the data. There are two kinds of usage patterns: (i) one that falls into a generic category of patterns; these are the patterns typically determined in a top down manner based on the expected user behavior when under particular mental state and expanded based on observed data; (ii) the other that falls into a specific (user-specific) category of patterns; these patterns are typically determined completely based on the observed data (without possibly a recourse to mental states). These models depict the abstracted behavior of users leading to an effective content (and in particular, ads) targeting. Note that the usage of the term “user” is in generic sense: for example, in a home context, “user” refers collectively to everybody in a home who operates a remote. The present invention addresses the issues of exploiting top-down (generic) models and discovering bottom-up (specific) models so as to effectively characterize a remote control unit usage.